CN116500086B - Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system - Google Patents

Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system Download PDF

Info

Publication number
CN116500086B
CN116500086B CN202310768087.6A CN202310768087A CN116500086B CN 116500086 B CN116500086 B CN 116500086B CN 202310768087 A CN202310768087 A CN 202310768087A CN 116500086 B CN116500086 B CN 116500086B
Authority
CN
China
Prior art keywords
heat dissipation
clad aluminum
copper clad
aluminum heat
qualified
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202310768087.6A
Other languages
Chinese (zh)
Other versions
CN116500086A (en
Inventor
李俊飞
王威威
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Xindianjin Photoelectric Technology Co ltd
Original Assignee
Shenzhen Xindianjin Photoelectric Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Xindianjin Photoelectric Technology Co ltd filed Critical Shenzhen Xindianjin Photoelectric Technology Co ltd
Priority to CN202310768087.6A priority Critical patent/CN116500086B/en
Publication of CN116500086A publication Critical patent/CN116500086A/en
Application granted granted Critical
Publication of CN116500086B publication Critical patent/CN116500086B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/20—Investigating or analyzing materials by the use of thermal means by investigating the development of heat, i.e. calorimetry, e.g. by measuring specific heat, by measuring thermal conductivity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/72—Investigating presence of flaws
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses a method and a system for evaluating production of a copper complex aluminum heat dissipation bottom plate based on deep learning, which are characterized in that a flaw detection model is built based on deep learning by acquiring image information of the copper complex aluminum heat dissipation bottom plate, flaw detection is carried out on the copper complex aluminum heat dissipation bottom plate to obtain a detection result, a heat dissipation performance detection system is built, a qualified copper complex aluminum heat dissipation bottom plate is acquired, thermal images of all areas on the qualified copper complex aluminum heat dissipation bottom plate are acquired, the thermal images are subjected to image processing to obtain temperature distribution conditions of all the areas, and the overall heat dissipation performance index of the qualified copper complex aluminum heat dissipation bottom plate is calculated and evaluated based on the temperature distribution to obtain a quantitative result of heat dissipation performance. The method can efficiently and accurately evaluate the quality condition of the copper clad aluminum heat radiation bottom plate by utilizing the deep learning technology to detect flaws, and simultaneously, the heat radiation performance detection system can comprehensively evaluate the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate by combining thermal imaging equipment and an image processing technology.

Description

Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system
Technical Field
The invention relates to the technical field of deep learning, in particular to a method and a system for evaluating production of a copper clad aluminum radiating bottom plate based on deep learning.
Background
The heat dissipation base plate is an important component widely used in electronic devices for heat dissipation and temperature control to ensure normal operation of electronic components. The copper clad aluminum heat dissipation base plate is composed of an aluminum substrate and copper foil, and has excellent heat conduction performance and mechanical strength. At present, the traditional quality detection method mainly depends on manual visual inspection, and the method has the problems of high subjectivity, low efficiency, easy generation of misjudgment and the like, and cannot meet the requirement of efficiently and accurately evaluating the quality of the radiating bottom plate. In recent years, deep learning techniques have made remarkable progress in the fields of image processing and pattern recognition. The deep learning model can learn complex characteristic representation and has high automation and generalization capability. This provides new possibilities for heat sink base plate production evaluation. By utilizing a deep learning algorithm, the image of the copper clad aluminum radiating bottom plate can be input into a model to detect flaws, so that the accurate classification and identification of flaw products and qualified products are realized, and the efficiency and accuracy of production quality control are improved. Therefore, there is a need for a deep learning-based copper clad aluminum heat dissipation base plate production evaluation method and system.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system.
The first aspect of the invention provides a deep learning-based copper complex aluminum heat dissipation base plate production evaluation method, which comprises the following steps:
preprocessing the copper clad aluminum heat dissipation base plate to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information;
constructing a flaw detection model based on deep learning, and guiding the image information of the copper clad aluminum heat radiation bottom plate into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified product information;
generating a production evaluation report according to the detection result;
constructing a heat radiation performance detection system, acquiring a qualified copper-clad aluminum heat radiation bottom plate according to qualified product information, and introducing the qualified copper-clad aluminum heat radiation bottom plate into the heat radiation performance detection system;
acquiring thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, and performing image processing on the thermal images to obtain temperature distribution of all areas;
and calculating the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate according to the temperature distribution, and evaluating according to the heat radiation performance index to obtain the heat radiation performance.
In this scheme, carry out the preliminary treatment to copper complex aluminium radiating basal plate, obtain copper complex aluminium radiating basal plate image information after the preliminary treatment, copper complex aluminium radiating basal plate historical image information after the preliminary treatment specifically does:
preparing a copper-clad aluminum heat dissipation base plate sample, coating magnetic powder on the copper-clad aluminum heat dissipation base plate, and then performing magnetic field excitation to obtain a pretreated copper-clad aluminum heat dissipation base plate sample;
placing a pretreated copper clad aluminum heat dissipation bottom plate sample in image acquisition equipment;
sequentially carrying out image acquisition on six surfaces of the pretreated copper clad aluminum heat dissipation base plate to obtain image information of the copper clad aluminum heat dissipation base plate;
and acquiring the history image information of the pretreated copper clad aluminum heat dissipation base plate, wherein the history image information comprises a pretreated flaw article history image and a qualified article history image.
In this scheme, based on the deep learning construction flaw detection model, with copper complex aluminum heat dissipation bottom plate image information leading-in flaw detection model detect, obtain the testing result, the testing result includes flaw article information, qualified article information, specifically does:
constructing a flaw detection model based on a deep learning technology;
extracting key characteristic information of a defective product historical image and a qualified product historical image, wherein the key characteristic information is first key characteristic information;
The first key characteristic information is guided into a flaw detection model for training;
the method comprises the steps of guiding image information of a copper clad aluminum heat dissipation base plate into a flaw detection model for detection, extracting key characteristic information of the image information of the copper clad aluminum heat dissipation base plate, wherein the key characteristic information of the image information of the copper clad aluminum heat dissipation base plate is second key characteristic information, comparing the first key characteristic information with the second key characteristic information to obtain a detection result, and the detection result comprises flaw information and qualified product information.
In this scheme, the production evaluation report is generated according to the detection result, specifically:
generating a flaw product report according to flaw product information, wherein the flaw product report comprises the type, the size and the position of flaws;
generating a qualified report according to qualified product information, wherein the qualified product report comprises the number of qualified products;
and integrating the flaw report and the qualified report to obtain a production evaluation report.
In this scheme, construct heat dispersion detecting system, according to the qualification information, acquire qualified copper complex aluminium heat dissipation bottom plate, with the leading-in heat dispersion detecting system of qualified copper complex aluminium heat dissipation bottom plate, specifically do:
constructing a heat radiation performance detection system, wherein the heat radiation performance detection system comprises thermal imaging equipment, a temperature sensor, a data acquisition device and a heating device;
Acquiring a qualified copper-clad aluminum heat dissipation base plate according to qualified product information;
leading the qualified copper clad aluminum heat dissipation bottom plate into a heat dissipation performance detection system for heating treatment;
the temperature sensor is connected to the good.
In this scheme, utilize thermal imaging equipment to acquire the thermal image of each region on the qualified copper complex aluminium radiating bottom plate, carry out image processing to the thermal image, obtain the temperature distribution in each region, specifically do:
scanning the qualified copper clad aluminum radiating bottom plate by using thermal imaging equipment to obtain a thermal image;
preprocessing the thermal image, wherein the preprocessing comprises image denoising, enhancing and correcting operations;
dividing and extracting features of the preprocessed thermal image by using a preset image processing algorithm to obtain temperature information of each region;
according to the temperature information, calculating the temperature distribution of each area on the qualified copper clad aluminum radiating bottom plate;
and (5) carrying out visualization treatment on the temperature distribution.
In this scheme, calculate the whole heat dispersion index of qualified copper complex aluminium heat dissipation bottom plate according to temperature distribution, evaluate according to heat dispersion index, obtain heat dispersion, specifically do:
according to the temperature distribution, calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate, wherein the heat radiation performance index comprises the highest temperature, the lowest temperature, the average temperature and the temperature gradient;
According to a preset evaluation standard, the heat radiation performance index is evaluated, and the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate is judged to be good or bad, so that an evaluation result is obtained;
and according to the evaluation result, obtaining the heat radiation performance evaluation of the qualified copper clad aluminum heat radiation bottom plate, wherein the heat radiation performance evaluation comprises excellent, good, qualified and unqualified.
The second aspect of the invention also provides a copper complex aluminum heat dissipation bottom plate production evaluation system based on deep learning, which comprises: the device comprises a memory and a processor, wherein the memory comprises a deep learning-based copper complex aluminum heat radiation bottom plate production evaluation method program, and when the deep learning-based copper complex aluminum heat radiation bottom plate production evaluation method program is executed by the processor, the following steps are realized:
preprocessing the copper clad aluminum heat dissipation base plate to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information;
constructing a flaw detection model based on deep learning, and guiding the image information of the copper clad aluminum heat radiation bottom plate into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified product information;
generating a production evaluation report according to the detection result;
Constructing a heat radiation performance detection system, acquiring a qualified copper-clad aluminum heat radiation bottom plate according to qualified product information, and introducing the qualified copper-clad aluminum heat radiation bottom plate into the heat radiation performance detection system;
acquiring thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, and performing image processing on the thermal images to obtain temperature distribution of all areas;
and calculating the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate according to the temperature distribution, and evaluating according to the heat radiation performance index to obtain the heat radiation performance.
In this scheme, based on the deep learning construction flaw detection model, with copper complex aluminum heat dissipation bottom plate image information leading-in flaw detection model detect, obtain the testing result, the testing result includes flaw article information, qualified article information, specifically does:
constructing a flaw detection model based on a deep learning technology;
extracting key characteristic information of a defective product historical image and a qualified product historical image, wherein the key characteristic information is first key characteristic information;
the first key characteristic information is guided into a flaw detection model for training;
the method comprises the steps of guiding image information of a copper clad aluminum heat dissipation base plate into a flaw detection model for detection, extracting key characteristic information of the image information of the copper clad aluminum heat dissipation base plate, wherein the key characteristic information of the image information of the copper clad aluminum heat dissipation base plate is second key characteristic information, comparing the first key characteristic information with the second key characteristic information to obtain a detection result, and the detection result comprises flaw information and qualified product information.
In this scheme, utilize thermal imaging equipment to acquire the thermal image of each region on the qualified copper complex aluminium radiating bottom plate, carry out image processing to the thermal image, obtain the temperature distribution in each region, specifically do:
scanning the qualified copper clad aluminum radiating bottom plate by using thermal imaging equipment to obtain a thermal image;
preprocessing the thermal image, wherein the preprocessing comprises image denoising, enhancing and correcting operations;
dividing and extracting features of the preprocessed thermal image by using an image processing algorithm to obtain temperature information of each region;
according to the temperature information, calculating the temperature distribution of each area on the qualified copper clad aluminum radiating bottom plate;
and (5) carrying out visualization treatment on the temperature distribution.
The invention discloses a method and a system for evaluating production of a copper complex aluminum heat dissipation bottom plate based on deep learning, which are characterized in that a flaw detection model is built based on deep learning by acquiring image information of the copper complex aluminum heat dissipation bottom plate, flaw detection is carried out on the copper complex aluminum heat dissipation bottom plate to obtain a detection result, a heat dissipation performance detection system is built, a qualified copper complex aluminum heat dissipation bottom plate is acquired, thermal images of all areas on the qualified copper complex aluminum heat dissipation bottom plate are acquired, the thermal images are subjected to image processing to obtain temperature distribution conditions of all the areas, and the overall heat dissipation performance index of the qualified copper complex aluminum heat dissipation bottom plate is calculated and evaluated based on the temperature distribution to obtain a quantitative result of heat dissipation performance. The method can efficiently and accurately evaluate the quality condition of the copper clad aluminum heat radiation bottom plate by utilizing the deep learning technology to detect flaws, and simultaneously, the heat radiation performance detection system can comprehensively evaluate the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate by combining thermal imaging equipment and an image processing technology.
Drawings
FIG. 1 shows a flow chart of a deep learning-based copper clad aluminum heat dissipation base plate production evaluation method;
FIG. 2 shows a flow chart of the detection of the copper clad aluminum heat dissipation base plate of the present application;
FIG. 3 shows a flow chart of the temperature profile obtained by the present application;
fig. 4 shows a block diagram of a deep learning-based copper clad aluminum heat dissipation base plate production evaluation system.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a deep learning-based copper clad aluminum heat dissipation base plate production evaluation method.
As shown in fig. 1, the first aspect of the present application provides a method for evaluating production of a copper clad aluminum heat dissipation base plate based on deep learning, comprising:
S102, preprocessing the copper clad aluminum heat dissipation base plate to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information;
s104, constructing a flaw detection model based on deep learning, and guiding the image information of the copper clad aluminum heat radiation bottom plate into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified product information;
s106, generating a production evaluation report according to the detection result;
s108, constructing a heat radiation performance detection system, acquiring a qualified copper clad aluminum heat radiation bottom plate according to qualified product information, and introducing the qualified copper clad aluminum heat radiation bottom plate into the heat radiation performance detection system;
s110, acquiring thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, and performing image processing on the thermal images to obtain temperature distribution of all areas;
s112, calculating the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate according to the temperature distribution, and evaluating according to the heat radiation performance index to obtain the heat radiation performance.
According to the embodiment of the invention, the copper complex aluminum heat dissipation base plate is preprocessed to obtain preprocessed copper complex aluminum heat dissipation base plate image information and preprocessed copper complex aluminum heat dissipation base plate historical image information, specifically:
Preparing a copper-clad aluminum heat dissipation base plate sample, coating magnetic powder on the copper-clad aluminum heat dissipation base plate, and then performing magnetic field excitation to obtain a pretreated copper-clad aluminum heat dissipation base plate sample;
placing a pretreated copper clad aluminum heat dissipation bottom plate sample in image acquisition equipment;
sequentially carrying out image acquisition on six surfaces of the pretreated copper clad aluminum heat dissipation base plate to obtain image information of the copper clad aluminum heat dissipation base plate;
and acquiring the history image information of the pretreated copper clad aluminum heat dissipation base plate, wherein the history image information comprises a pretreated flaw article history image and a qualified article history image.
The method is characterized in that the coated magnetic powder is prepared by dissolving the magnetic powder in a solvent, and then uniformly coating the magnetic powder on the surface of a copper-clad aluminum heat dissipation base plate by using a spraying method; the magnetic field excitation is to apply a uniform magnetic field on the copper clad aluminum heat dissipation base plate coated with magnetic powder by using an electromagnet, so that the magnetic powder forms a regular magnetic pattern on the surface of the copper clad aluminum heat dissipation base plate; the pretreated historical image information of the copper-clad aluminum heat dissipation base plate refers to historical images of different magnetic powder distribution shapes formed after magnetic field excitation by coating magnetic powder on the surfaces of the defective copper-clad aluminum heat dissipation base plate and the qualified copper-clad aluminum heat dissipation base plate; by observing the magnetic pattern, defects of the copper clad aluminum heat dissipation base plate can be identified, wherein the defects comprise cracks, bubbles and pits.
Fig. 2 shows a flow chart of the detection of the copper clad aluminum heat dissipation base plate according to the present invention.
According to the embodiment of the invention, a flaw detection model is constructed based on deep learning, and the image information of the copper clad aluminum heat radiation bottom plate is guided into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified information, and specifically comprises the following steps:
s202, constructing a flaw detection model based on a deep learning technology;
s204, extracting key feature information of a defective product historical image and a qualified product historical image, wherein the key feature information is first key feature information;
s206, the first key characteristic information is imported into a flaw detection model for training;
s208, the image information of the copper clad aluminum heat dissipation bottom plate is guided into a flaw detection model for detection, key characteristic information of the image information of the copper clad aluminum heat dissipation bottom plate is extracted, the key characteristic information of the image information of the copper clad aluminum heat dissipation bottom plate is second key characteristic information, the first key characteristic information is compared with the second key characteristic information, and a detection result is obtained, wherein the detection result comprises flaw information and qualified product information.
The image information of the copper clad aluminum heat dissipation bottom plate is guided into a flaw detection model for detection, so that whether the copper clad aluminum heat dissipation bottom plate has flaws can be judged; the deep learning technology refers to a convolutional neural network, and image features are extracted and classified through components such as a convolutional layer, a pooling layer, a full-connection layer and the like; the key characteristic information refers to the distribution condition and the distribution shape of the magnetic powder; the first key characteristic information is guided into a flaw detection model for training, and in the training process, the model learns the characteristics of defective products and qualified products so as to perform accurate flaw detection; the defect detection model analyzes whether the pretreated copper clad aluminum heat dissipation bottom plate has defects or not by learning the magnetic powder distribution condition of the history defect copper clad aluminum heat dissipation bottom plate.
According to the embodiment of the invention, the production evaluation report is generated according to the detection result, specifically:
generating a flaw product report according to flaw product information, wherein the flaw product report comprises the type, the size and the position of flaws;
generating a qualified report according to qualified product information, wherein the qualified product report comprises the number of qualified products;
and integrating the flaw report and the qualified report to obtain a production evaluation report.
By generating the evaluation report, the production quality of the copper clad aluminum heat dissipation base plate can be comprehensively evaluated and monitored, guidance and improved basis are provided for the production process, and the quality and performance of the product are ensured to meet the requirements.
According to the embodiment of the invention, the heat radiation performance detection system is constructed, the qualified copper-clad aluminum heat radiation bottom plate is obtained according to qualified product information, and the qualified copper-clad aluminum heat radiation bottom plate is led into the heat radiation performance detection system, specifically:
constructing a heat radiation performance detection system, wherein the heat radiation performance detection system comprises thermal imaging equipment, a temperature sensor, a data acquisition device and a heating device;
acquiring a qualified copper-clad aluminum heat dissipation base plate according to qualified product information;
leading the qualified copper clad aluminum heat dissipation bottom plate into a heat dissipation performance detection system for heating treatment;
The temperature sensor is connected to the good.
The thermal imaging device is used for detecting the temperature distribution condition of the surface of the radiating bottom plate. The method can generate thermal imaging images through measurement of infrared radiation, and display temperature differences of different areas; the temperature sensor is used for monitoring the temperature change of the radiating bottom plate in real time. By connecting the temperature sensor to the qualified copper clad aluminum heat dissipation base plate, the temperature of the heat dissipation base plate can be accurately measured; the heating device is used for heating the qualified copper clad aluminum radiating bottom plate. By controlling the heating power and time of the heating device, the heat load in actual use can be simulated to evaluate the heat dissipation performance of the heat dissipation base plate.
Fig. 3 shows a flow chart of the present invention for obtaining a temperature profile.
According to the embodiment of the invention, the thermal imaging equipment is used for acquiring the thermal image of each region on the qualified copper clad aluminum radiating base plate, and the thermal image is subjected to image processing to obtain the temperature distribution of each region, specifically:
s302, scanning a qualified copper clad aluminum heat dissipation base plate by using thermal imaging equipment to obtain a thermal image;
s304, preprocessing the thermal image, wherein the preprocessing comprises image denoising, enhancement and correction operations;
S306, segmenting and extracting features of the preprocessed thermal image by using a preset image processing algorithm to obtain temperature information of each region;
s308, calculating the temperature distribution of each area on the qualified copper clad aluminum heat dissipation base plate according to the temperature information;
and S310, performing visualization processing on the temperature distribution.
It should be noted that, the enhancement operation may improve contrast and details of the image, and the correction operation may correct color shift and geometric distortion of the thermal image; and dividing and extracting features of the preprocessed thermal image by using a preset image processing algorithm. The segmentation operation divides the thermal image into different regions or objects in order to analyze the temperature conditions of each region. The feature extraction operation extracts temperature information of each region by identifying a specific pattern or structure in the thermal image; the temperature distribution is subjected to visualization treatment, and the visualization mode comprises the steps of drawing a temperature heat map, an isothermal line and a temperature distribution map, so that the temperature condition of each area on the qualified copper clad aluminum heat dissipation base plate can be intuitively displayed, and operators can be helped to understand and analyze the heat dissipation performance; the preset image processing algorithm comprises an image segmentation algorithm and a histogram equalization algorithm.
According to the embodiment of the invention, the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate is calculated according to the temperature distribution, and is evaluated according to the heat radiation performance index to obtain the heat radiation performance, specifically:
according to the temperature distribution, calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate, wherein the heat radiation performance index comprises the highest temperature, the lowest temperature, the average temperature and the temperature gradient;
according to a preset evaluation standard, the heat radiation performance index is evaluated, and the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate is judged to be good or bad, so that an evaluation result is obtained;
and according to the evaluation result, obtaining the heat radiation performance evaluation of the qualified copper clad aluminum heat radiation bottom plate, wherein the heat radiation performance evaluation comprises excellent, good, qualified and unqualified.
The highest temperature reflects the region of the base plate where the temperature is highest, the lowest temperature indicates the region of the base plate where the temperature is lowest, the average temperature is the average value of the temperatures of all the regions, and the temperature gradient indicates the rate of change of the temperature on the base plate.
According to an embodiment of the present invention, further comprising:
obtaining a semi-finished workpiece before rolling and forming a copper clad aluminum heat dissipation bottom plate;
heating the semi-finished workpiece, and keeping a constant temperature;
Acquiring a thermal image of the semi-finished workpiece, and measuring the temperature distribution of the surface of the semi-finished workpiece;
calculating the average temperature and the heat dissipation power of the semi-finished workpiece based on the temperature distribution;
calculating the heat dissipation coefficient of the semi-finished workpiece according to the average temperature and the heat dissipation power, and judging the heat dissipation performance index of the semi-finished workpiece;
and if the heat radiation performance index does not reach the preset index, reprocessing the semi-finished product.
It should be noted that, in the embodiment of the invention, before the copper clad aluminum heat dissipation bottom plate is processed and formed, the semi-finished workpiece is detected, if the semi-finished workpiece does not reach the processing and forming standard, the semi-finished workpiece is re-processed and then detected again until the processing and forming standard is met, thereby being beneficial to improving the quality of the copper clad aluminum heat dissipation bottom plate and reducing the economic loss caused by unqualified products; the obtained thermal image of the semi-finished workpiece is beneficial to monitoring the temperature change of the semi-finished workpiece; the preset index refers to a heat dissipation coefficient threshold set by a copper complex aluminum heat dissipation base plate production standard.
According to an embodiment of the present invention, further comprising:
acquiring image information of the pretreated copper clad aluminum heat dissipation bottom plate;
detecting the defect position and type of the copper clad aluminum radiating bottom plate according to the image information to obtain a detection result;
Determining a repair strategy according to the detection result;
according to a repairing strategy, applying a heat source to the defect position through a laser heating technology, controlling the temperature and heating time of the heat source, and automatically repairing the defect position at a preset temperature;
and detecting the repairing result of the repaired copper clad aluminum heat dissipation bottom plate.
In addition, the embodiment of the invention automatically repairs the defects of the copper clad aluminum radiating bottom plate under the condition that the defects are detected by the copper clad aluminum radiating bottom plate, reduces the rejection rate of the copper clad aluminum radiating bottom plate and reduces the production cost; the repair strategy is different repair schemes formulated according to defect types, and comprises temperature control and heating time, wherein the different defect repair temperatures and heating time are different; the repair result refers to whether the repair result reaches the standard or not.
According to an embodiment of the present invention, further comprising:
collecting images of six faces of a defective copper clad aluminum heat dissipation bottom plate;
importing the image into a defect detection model, and determining the type, size and position of the defect;
carrying out heating treatment on the copper-clad aluminum radiating bottom plate with the defects to obtain a thermal image of the copper-clad aluminum radiating bottom plate with the defects;
Acquiring the temperature distribution of each surface according to the thermal image, and analyzing the heat radiation performance of each surface according to the temperature distribution;
acquiring the heat dissipation performance of each surface of the qualified product;
comparing the heat radiation performance of each surface of the copper complex aluminum heat radiation bottom plate with the heat radiation performance of the surface of the same position of the qualified product to obtain a defect influence coefficient of each surface;
and generating production evaluation standards of different surfaces according to the defect influence coefficient of each surface.
It should be noted that, in the embodiment of the present invention, the influence of the flaws of each surface on the heat dissipation performance is detected, so as to obtain an influence coefficient, the smaller the influence is, the lower the influence coefficient is, if the influence of the flaws of the surface on the heat dissipation performance is smaller, the flaws of the surface can be not processed, and if the influence is larger, reworking processing is performed; according to the embodiment of the invention, the processing of the surface with defects can be greatly reduced, and the production efficiency is improved; the defect influence coefficients comprise influence coefficients of defect types, sizes and positions on heat radiation performance of each surface; the production evaluation standard is a production standard for each surface of the copper clad aluminum heat dissipation base plate; the embodiment of the invention is also suitable for the production evaluation standards of other types of heat dissipation base plate products.
Fig. 4 shows a block diagram of a deep learning-based copper clad aluminum heat dissipation base plate production evaluation system.
The second aspect of the invention also provides a deep learning-based copper complex aluminum heat dissipation base plate production evaluation system 4, which comprises: the memory 41 and the processor 42, wherein the memory comprises a deep learning-based copper complex aluminum heat dissipation base plate production evaluation method program, and when the deep learning-based copper complex aluminum heat dissipation base plate production evaluation method program is executed by the processor, the following steps are realized:
preprocessing the copper clad aluminum heat dissipation base plate to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information;
constructing a flaw detection model based on deep learning, and guiding the image information of the copper clad aluminum heat radiation bottom plate into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified product information;
generating a production evaluation report according to the detection result;
constructing a heat radiation performance detection system, acquiring a qualified copper-clad aluminum heat radiation bottom plate according to qualified product information, and introducing the qualified copper-clad aluminum heat radiation bottom plate into the heat radiation performance detection system;
Acquiring thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, and performing image processing on the thermal images to obtain temperature distribution of all areas;
and calculating the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate according to the temperature distribution, and evaluating according to the heat radiation performance index to obtain the heat radiation performance.
According to the embodiment of the invention, the copper complex aluminum heat dissipation base plate is preprocessed to obtain preprocessed copper complex aluminum heat dissipation base plate image information and preprocessed copper complex aluminum heat dissipation base plate historical image information, specifically:
preparing a copper-clad aluminum heat dissipation base plate sample, coating magnetic powder on the copper-clad aluminum heat dissipation base plate, and then performing magnetic field excitation to obtain a pretreated copper-clad aluminum heat dissipation base plate sample;
placing a pretreated copper clad aluminum heat dissipation bottom plate sample in image acquisition equipment;
sequentially carrying out image acquisition on six surfaces of the pretreated copper clad aluminum heat dissipation base plate to obtain image information of the copper clad aluminum heat dissipation base plate;
and acquiring the history image information of the pretreated copper clad aluminum heat dissipation base plate, wherein the history image information comprises a pretreated flaw article history image and a qualified article history image.
The method is characterized in that the coated magnetic powder is prepared by dissolving the magnetic powder in a solvent, and then uniformly coating the magnetic powder on the surface of a copper-clad aluminum heat dissipation base plate by using a spraying method; the magnetic field excitation is to apply a uniform magnetic field on the copper clad aluminum heat dissipation base plate coated with magnetic powder by using an electromagnet, so that the magnetic powder forms a regular magnetic pattern on the surface of the copper clad aluminum heat dissipation base plate; the pretreated historical image information of the copper-clad aluminum heat dissipation base plate refers to historical images of different magnetic powder distribution shapes formed after magnetic field excitation by coating magnetic powder on the surfaces of the defective copper-clad aluminum heat dissipation base plate and the qualified copper-clad aluminum heat dissipation base plate; by observing the magnetic pattern, defects of the copper clad aluminum heat dissipation base plate can be identified, wherein the defects comprise cracks, bubbles and pits.
According to the embodiment of the invention, a flaw detection model is constructed based on deep learning, and the image information of the copper clad aluminum heat radiation bottom plate is guided into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified information, and specifically comprises the following steps:
constructing a flaw detection model based on a deep learning technology;
extracting key characteristic information of a defective product historical image and a qualified product historical image, wherein the key characteristic information is first key characteristic information;
the first key characteristic information is guided into a flaw detection model for training;
the method comprises the steps of guiding image information of a copper clad aluminum heat dissipation base plate into a flaw detection model for detection, extracting key characteristic information of the image information of the copper clad aluminum heat dissipation base plate, wherein the key characteristic information of the image information of the copper clad aluminum heat dissipation base plate is second key characteristic information, comparing the first key characteristic information with the second key characteristic information to obtain a detection result, and the detection result comprises flaw information and qualified product information.
The image information of the copper clad aluminum heat dissipation bottom plate is guided into a flaw detection model for detection, so that whether the copper clad aluminum heat dissipation bottom plate has flaws can be judged; the deep learning technology refers to a convolutional neural network, and image features are extracted and classified through components such as a convolutional layer, a pooling layer, a full-connection layer and the like; the key characteristic information refers to the distribution condition and the distribution shape of the magnetic powder; the first key characteristic information is guided into a flaw detection model for training, and in the training process, the model learns the characteristics of defective products and qualified products so as to perform accurate flaw detection; the defect detection model analyzes whether the pretreated copper clad aluminum heat dissipation bottom plate has defects or not by learning the magnetic powder distribution condition of the history defect copper clad aluminum heat dissipation bottom plate.
According to the embodiment of the invention, the production evaluation report is generated according to the detection result, specifically:
generating a flaw product report according to flaw product information, wherein the flaw product report comprises the type, the size and the position of flaws;
generating a qualified report according to qualified product information, wherein the qualified product report comprises the number of qualified products;
and integrating the flaw report and the qualified report to obtain a production evaluation report.
By generating the evaluation report, the production quality of the copper clad aluminum heat dissipation base plate can be comprehensively evaluated and monitored, guidance and improved basis are provided for the production process, and the quality and performance of the product are ensured to meet the requirements.
According to the embodiment of the invention, the heat radiation performance detection system is constructed, the qualified copper-clad aluminum heat radiation bottom plate is obtained according to qualified product information, and the qualified copper-clad aluminum heat radiation bottom plate is led into the heat radiation performance detection system, specifically:
constructing a heat radiation performance detection system, wherein the heat radiation performance detection system comprises thermal imaging equipment, a temperature sensor, a data acquisition device and a heating device;
acquiring a qualified copper-clad aluminum heat dissipation base plate according to qualified product information;
leading the qualified copper clad aluminum heat dissipation bottom plate into a heat dissipation performance detection system for heating treatment;
The temperature sensor is connected to the good.
The thermal imaging device is used for detecting the temperature distribution condition of the surface of the radiating bottom plate. The method can generate thermal imaging images through measurement of infrared radiation, and display temperature differences of different areas; the temperature sensor is used for monitoring the temperature change of the radiating bottom plate in real time. By connecting the temperature sensor to the qualified copper clad aluminum heat dissipation base plate, the temperature of the heat dissipation base plate can be accurately measured; the heating device is used for heating the qualified copper clad aluminum radiating bottom plate. By controlling the heating power and time of the heating device, the heat load in actual use can be simulated to evaluate the heat dissipation performance of the heat dissipation base plate.
According to the embodiment of the invention, the thermal imaging equipment is used for acquiring the thermal image of each region on the qualified copper clad aluminum radiating base plate, and the thermal image is subjected to image processing to obtain the temperature distribution of each region, specifically:
scanning the qualified copper clad aluminum radiating bottom plate by using thermal imaging equipment to obtain a thermal image;
preprocessing the thermal image, wherein the preprocessing comprises image denoising, enhancing and correcting operations;
Dividing and extracting features of the preprocessed thermal image by using a preset image processing algorithm to obtain temperature information of each region;
according to the temperature information, calculating the temperature distribution of each area on the qualified copper clad aluminum radiating bottom plate;
and (5) carrying out visualization treatment on the temperature distribution.
It should be noted that, the enhancement operation may improve contrast and details of the image, and the correction operation may correct color shift and geometric distortion of the thermal image; and dividing and extracting features of the preprocessed thermal image by using a preset image processing algorithm. The segmentation operation divides the thermal image into different regions or objects in order to analyze the temperature conditions of each region. The feature extraction operation extracts temperature information of each region by identifying a specific pattern or structure in the thermal image; the temperature distribution is subjected to visualization processing, and the visualization mode comprises the steps of drawing a temperature heat map, an isothermal line and a temperature distribution map, so that the temperature condition of each area on the qualified copper-aluminum composite heat dissipation base plate can be intuitively displayed, and an operator is helped to understand and analyze the heat dissipation performance.
According to the embodiment of the invention, the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate is calculated according to the temperature distribution, and is evaluated according to the heat radiation performance index to obtain the heat radiation performance, specifically:
According to the temperature distribution, calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate, wherein the heat radiation performance index comprises the highest temperature, the lowest temperature, the average temperature and the temperature gradient;
according to a preset evaluation standard, the heat radiation performance index is evaluated, and the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate is judged to be good or bad, so that an evaluation result is obtained;
and according to the evaluation result, obtaining the heat radiation performance evaluation of the qualified copper clad aluminum heat radiation bottom plate, wherein the heat radiation performance evaluation comprises excellent, good, qualified and unqualified.
The highest temperature reflects the region of the base plate where the temperature is highest, the lowest temperature indicates the region of the base plate where the temperature is lowest, the average temperature is the average value of the temperatures of all the regions, and the temperature gradient indicates the rate of change of the temperature on the base plate.
According to an embodiment of the present invention, further comprising:
obtaining a semi-finished workpiece before rolling and forming a copper clad aluminum heat dissipation bottom plate;
heating the semi-finished workpiece, and keeping a constant temperature;
acquiring a thermal image of the semi-finished workpiece, and measuring the temperature distribution of the surface of the semi-finished workpiece;
calculating the average temperature and the heat dissipation power of the semi-finished workpiece based on the temperature distribution;
Calculating the heat dissipation coefficient of the semi-finished workpiece according to the average temperature and the heat dissipation power, and judging the heat dissipation performance index of the semi-finished workpiece;
and if the heat radiation performance index does not reach the preset index, reprocessing the semi-finished product.
It should be noted that, in the embodiment of the invention, before the copper clad aluminum heat dissipation bottom plate is processed and formed, the semi-finished workpiece is detected, if the semi-finished workpiece does not reach the processing and forming standard, the semi-finished workpiece is re-processed and then detected again until the processing and forming standard is met, thereby being beneficial to improving the quality of the copper clad aluminum heat dissipation bottom plate and reducing the economic loss caused by unqualified products; the obtained thermal image of the semi-finished workpiece is beneficial to monitoring the temperature change of the semi-finished workpiece; the preset index refers to a heat dissipation coefficient threshold set by a copper complex aluminum heat dissipation base plate production standard.
According to an embodiment of the present invention, further comprising:
acquiring image information of the pretreated copper clad aluminum heat dissipation bottom plate;
detecting the defect position and type of the copper clad aluminum radiating bottom plate according to the image information;
determining a repair strategy according to the detection result;
applying a heat source to the defect position by a laser heating technology, and controlling the temperature and heating time of the heat source;
According to the repair strategy, automatically repairing the defect position at a preset temperature;
and detecting the repairing result of the repaired copper clad aluminum heat dissipation bottom plate.
In addition, the embodiment of the invention automatically repairs the defects of the copper clad aluminum radiating bottom plate under the condition that the defects are detected by the copper clad aluminum radiating bottom plate, reduces the rejection rate of the copper clad aluminum radiating bottom plate and reduces the production cost; the repair strategy is different repair schemes formulated according to defect types; the repair result refers to whether the repair result reaches the standard or not.
According to an embodiment of the present invention, further comprising:
collecting images of six faces of a defective copper clad aluminum heat dissipation bottom plate;
importing the image into a defect detection model, and determining the type, size and position of the defect;
carrying out heating treatment on the copper-clad aluminum radiating bottom plate with the defects to obtain a thermal image of the copper-clad aluminum radiating bottom plate with the defects;
acquiring the temperature distribution of each surface according to the thermal image, and analyzing the heat radiation performance of each surface according to the temperature distribution;
acquiring the heat dissipation performance of each surface of the qualified product;
comparing the heat radiation performance of each surface of the copper complex aluminum heat radiation bottom plate with the heat radiation performance of the surface of the same position of the qualified product to obtain a defect influence coefficient of each surface;
And generating production evaluation standards of different surfaces according to the defect influence coefficient of each surface.
It should be noted that, in the embodiment of the present invention, the influence of the flaws of each surface on the heat dissipation performance is detected, so as to obtain an influence coefficient, the smaller the influence is, the lower the influence coefficient is, if the influence of the flaws of the surface on the heat dissipation performance is smaller, the flaws of the surface can be not processed, and if the influence is larger, reworking processing is performed; according to the embodiment of the invention, the processing of the surface with defects can be greatly reduced, and the production efficiency is improved; the defect influence coefficients comprise influence coefficients of defect types, sizes and positions on heat radiation performance of each surface; the production evaluation standard is a production standard for each surface of the copper clad aluminum heat dissipation base plate; the embodiment of the invention is also suitable for the production evaluation standards of other types of heat dissipation base plate products.
The invention discloses a method and a system for evaluating production of a copper complex aluminum heat dissipation bottom plate based on deep learning, which are characterized in that a flaw detection model is built based on deep learning by acquiring image information of the copper complex aluminum heat dissipation bottom plate, flaw detection is carried out on the copper complex aluminum heat dissipation bottom plate to obtain a detection result, a heat dissipation performance detection system is built, a qualified copper complex aluminum heat dissipation bottom plate is acquired, thermal images of all areas on the qualified copper complex aluminum heat dissipation bottom plate are acquired, the thermal images are subjected to image processing to obtain temperature distribution conditions of all the areas, and the overall heat dissipation performance index of the qualified copper complex aluminum heat dissipation bottom plate is calculated and evaluated based on the temperature distribution to obtain a quantitative result of heat dissipation performance. The method can efficiently and accurately evaluate the quality condition of the copper clad aluminum heat radiation bottom plate by utilizing the deep learning technology to detect flaws, and simultaneously, the heat radiation performance detection system can comprehensively evaluate the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate by combining thermal imaging equipment and an image processing technology.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (7)

1. The production evaluation method of the copper complex aluminum heat dissipation base plate based on deep learning is characterized by comprising the following steps of:
preprocessing the copper clad aluminum heat dissipation base plate to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information;
constructing a flaw detection model based on deep learning, and guiding the image information of the copper clad aluminum heat radiation bottom plate into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified product information;
generating a production evaluation report according to the detection result;
constructing a heat radiation performance detection system, acquiring a qualified copper-clad aluminum heat radiation bottom plate according to qualified product information, and introducing the qualified copper-clad aluminum heat radiation bottom plate into the heat radiation performance detection system;
Acquiring thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, and performing image processing on the thermal images to obtain temperature distribution of all areas;
calculating the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate according to the temperature distribution, and evaluating according to the heat radiation performance index to obtain the heat radiation performance;
the method comprises the steps of obtaining thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, carrying out image processing on the thermal images to obtain temperature distribution of all areas, and specifically comprises the following steps:
scanning the qualified copper clad aluminum radiating bottom plate by using thermal imaging equipment to obtain a thermal image;
preprocessing the thermal image, wherein the preprocessing comprises image denoising, enhancing and correcting operations;
dividing and extracting features of the preprocessed thermal image by using a preset image processing algorithm to obtain temperature information of each region;
according to the temperature information, calculating the temperature distribution of each area on the qualified copper clad aluminum radiating bottom plate;
carrying out visual treatment on the temperature distribution;
the method comprises the steps of calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate according to temperature distribution, evaluating according to the heat radiation performance index, and obtaining the heat radiation performance, wherein the method specifically comprises the following steps:
According to the temperature distribution, calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate, wherein the heat radiation performance index comprises the highest temperature, the lowest temperature, the average temperature and the temperature gradient;
according to a preset evaluation standard, the heat radiation performance index is evaluated, and the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate is judged to be good or bad, so that an evaluation result is obtained;
according to the evaluation result, obtaining the heat radiation performance evaluation of the qualified copper clad aluminum heat radiation bottom plate, wherein the heat radiation performance evaluation comprises excellent, good, qualified and unqualified;
wherein further comprising:
obtaining a semi-finished workpiece before rolling and forming a copper clad aluminum heat dissipation bottom plate;
heating the semi-finished workpiece, and keeping a constant temperature;
acquiring a thermal image of the semi-finished workpiece, and measuring the temperature distribution of the surface of the semi-finished workpiece;
calculating the average temperature and the heat dissipation power of the semi-finished workpiece based on the temperature distribution;
calculating the heat dissipation coefficient of the semi-finished workpiece according to the average temperature and the heat dissipation power, and judging the heat dissipation performance index of the semi-finished workpiece;
if the heat radiation performance index does not reach the preset index, reprocessing the semi-finished product;
wherein, still include:
acquiring image information of the pretreated copper clad aluminum heat dissipation bottom plate;
Detecting the defect position and type of the copper clad aluminum radiating bottom plate according to the image information;
determining a repair strategy according to the detection result;
applying a heat source to the defect position by a laser heating technology, and controlling the temperature and heating time of the heat source;
according to the repair strategy, automatically repairing the defect position at a preset temperature;
and detecting the repairing result of the repaired copper clad aluminum heat dissipation bottom plate.
2. The method for evaluating the production of the copper clad aluminum heat dissipation base plate based on deep learning according to claim 1, wherein the method is characterized in that the copper clad aluminum heat dissipation base plate is preprocessed to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information, and specifically comprises the following steps:
preparing a copper-clad aluminum heat dissipation base plate sample, coating magnetic powder on the copper-clad aluminum heat dissipation base plate, and then performing magnetic field excitation to obtain a pretreated copper-clad aluminum heat dissipation base plate sample;
placing a pretreated copper clad aluminum heat dissipation bottom plate sample in image acquisition equipment;
sequentially carrying out image acquisition on six surfaces of the pretreated copper clad aluminum heat dissipation base plate to obtain image information of the copper clad aluminum heat dissipation base plate;
and acquiring the history image information of the pretreated copper clad aluminum heat dissipation base plate, wherein the history image information comprises a pretreated flaw article history image and a qualified article history image.
3. The method for evaluating production of the copper clad aluminum heat dissipation base plate based on deep learning according to claim 2, wherein the method is characterized in that a flaw detection model is constructed based on deep learning, image information of the copper clad aluminum heat dissipation base plate is led into the flaw detection model for detection, and a detection result is obtained, wherein the detection result comprises flaw information and qualified product information, and specifically comprises the following steps:
constructing a flaw detection model based on a deep learning technology;
extracting key characteristic information of a defective product historical image and a qualified product historical image, wherein the key characteristic information is first key characteristic information;
the first key characteristic information is guided into a flaw detection model for training;
the method comprises the steps of guiding image information of a copper clad aluminum heat dissipation base plate into a flaw detection model for detection, extracting key characteristic information of the image information of the copper clad aluminum heat dissipation base plate, wherein the key characteristic information of the image information of the copper clad aluminum heat dissipation base plate is second key characteristic information, comparing the first key characteristic information with the second key characteristic information to obtain a detection result, and the detection result comprises flaw information and qualified product information.
4. The deep learning-based copper clad aluminum heat dissipation base plate production evaluation method according to claim 1, wherein the production evaluation report is generated according to the detection result, specifically:
Generating a flaw product report according to flaw product information, wherein the flaw product report comprises the type, the size and the position of flaws;
generating a qualified report according to qualified product information, wherein the qualified product report comprises the number of qualified products;
and integrating the flaw report and the qualified report to obtain a production evaluation report.
5. The method for evaluating production of copper clad aluminum radiating bottom plates based on deep learning according to claim 1, wherein the step of constructing a radiating performance detection system, obtaining a qualified copper clad aluminum radiating bottom plate according to qualified product information, and introducing the qualified copper clad aluminum radiating bottom plate into the radiating performance detection system comprises the following steps:
constructing a heat radiation performance detection system, wherein the heat radiation performance detection system comprises thermal imaging equipment, a temperature sensor, a data acquisition device and a heating device;
acquiring a qualified copper-clad aluminum heat dissipation base plate according to qualified product information;
leading the qualified copper clad aluminum heat dissipation bottom plate into a heat dissipation performance detection system for heating treatment;
the temperature sensor is connected to the good.
6. The deep learning-based copper complex aluminum heat dissipation base plate production evaluation system is characterized by comprising a storage and a processor, wherein the storage comprises a deep learning-based copper complex aluminum heat dissipation base plate production evaluation method program, and when the deep learning-based copper complex aluminum heat dissipation base plate production evaluation method program is executed by the processor, the following steps are realized:
Preprocessing the copper clad aluminum heat dissipation base plate to obtain preprocessed copper clad aluminum heat dissipation base plate image information and preprocessed copper clad aluminum heat dissipation base plate historical image information;
constructing a flaw detection model based on deep learning, and guiding the image information of the copper clad aluminum heat radiation bottom plate into the flaw detection model for detection to obtain a detection result, wherein the detection result comprises flaw information and qualified product information;
generating a production evaluation report according to the detection result;
constructing a heat radiation performance detection system, acquiring a qualified copper-clad aluminum heat radiation bottom plate according to qualified product information, and introducing the qualified copper-clad aluminum heat radiation bottom plate into the heat radiation performance detection system;
acquiring thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, and performing image processing on the thermal images to obtain temperature distribution of all areas;
calculating the overall heat radiation performance index of the qualified copper clad aluminum heat radiation bottom plate according to the temperature distribution, and evaluating according to the heat radiation performance index to obtain the heat radiation performance;
the method comprises the steps of obtaining thermal images of all areas on the qualified copper clad aluminum radiating base plate by using thermal imaging equipment, carrying out image processing on the thermal images to obtain temperature distribution of all areas, and specifically comprises the following steps:
Scanning the qualified copper clad aluminum radiating bottom plate by using thermal imaging equipment to obtain a thermal image;
preprocessing the thermal image, wherein the preprocessing comprises image denoising, enhancing and correcting operations;
dividing and extracting features of the preprocessed thermal image by using a preset image processing algorithm to obtain temperature information of each region;
according to the temperature information, calculating the temperature distribution of each area on the qualified copper clad aluminum radiating bottom plate;
carrying out visual treatment on the temperature distribution;
the method comprises the steps of calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate according to temperature distribution, evaluating according to the heat radiation performance index, and obtaining the heat radiation performance, wherein the method specifically comprises the following steps:
according to the temperature distribution, calculating the overall heat radiation performance index of the qualified copper complex aluminum heat radiation bottom plate, wherein the heat radiation performance index comprises the highest temperature, the lowest temperature, the average temperature and the temperature gradient;
according to a preset evaluation standard, the heat radiation performance index is evaluated, and the heat radiation performance of the qualified copper clad aluminum heat radiation bottom plate is judged to be good or bad, so that an evaluation result is obtained;
according to the evaluation result, obtaining the heat radiation performance evaluation of the qualified copper clad aluminum heat radiation bottom plate, wherein the heat radiation performance evaluation comprises excellent, good, qualified and unqualified;
Wherein further comprising:
obtaining a semi-finished workpiece before rolling and forming a copper clad aluminum heat dissipation bottom plate;
heating the semi-finished workpiece, and keeping a constant temperature;
acquiring a thermal image of the semi-finished workpiece, and measuring the temperature distribution of the surface of the semi-finished workpiece;
calculating the average temperature and the heat dissipation power of the semi-finished workpiece based on the temperature distribution;
calculating the heat dissipation coefficient of the semi-finished workpiece according to the average temperature and the heat dissipation power, and judging the heat dissipation performance index of the semi-finished workpiece;
if the heat radiation performance index does not reach the preset index, reprocessing the semi-finished product;
wherein, still include:
acquiring image information of the pretreated copper clad aluminum heat dissipation bottom plate;
detecting the defect position and type of the copper clad aluminum radiating bottom plate according to the image information;
determining a repair strategy according to the detection result;
applying a heat source to the defect position by a laser heating technology, and controlling the temperature and heating time of the heat source;
according to the repair strategy, automatically repairing the defect position at a preset temperature;
and detecting the repairing result of the repaired copper clad aluminum heat dissipation bottom plate.
7. The deep learning-based copper clad aluminum heat dissipation base plate production evaluation system according to claim 6, wherein the deep learning-based defect detection model is constructed, the image information of the copper clad aluminum heat dissipation base plate is guided into the defect detection model for detection, and a detection result is obtained, wherein the detection result comprises defect product information and qualified product information, and specifically comprises:
Constructing a flaw detection model based on a deep learning technology;
extracting key characteristic information of a defective product historical image and a qualified product historical image, wherein the key characteristic information is first key characteristic information;
the first key characteristic information is guided into a flaw detection model for training;
the method comprises the steps of guiding image information of a copper clad aluminum heat dissipation base plate into a flaw detection model for detection, extracting key characteristic information of the image information of the copper clad aluminum heat dissipation base plate, wherein the key characteristic information of the image information of the copper clad aluminum heat dissipation base plate is second key characteristic information, comparing the first key characteristic information with the second key characteristic information to obtain a detection result, and the detection result comprises flaw information and qualified product information.
CN202310768087.6A 2023-06-28 2023-06-28 Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system Active CN116500086B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310768087.6A CN116500086B (en) 2023-06-28 2023-06-28 Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310768087.6A CN116500086B (en) 2023-06-28 2023-06-28 Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system

Publications (2)

Publication Number Publication Date
CN116500086A CN116500086A (en) 2023-07-28
CN116500086B true CN116500086B (en) 2023-09-05

Family

ID=87330527

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310768087.6A Active CN116500086B (en) 2023-06-28 2023-06-28 Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system

Country Status (1)

Country Link
CN (1) CN116500086B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116776647B (en) * 2023-08-21 2024-01-16 深圳市鑫冠亚科技有限公司 Performance prediction method and system for composite nickel-copper-aluminum heat dissipation bottom plate
CN116862913B (en) * 2023-09-04 2023-12-22 深圳市鑫典金光电科技有限公司 Machine vision-based defect detection method and system for composite nickel-copper heat dissipation bottom plate

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103226731A (en) * 2013-03-20 2013-07-31 太原理工大学 Online thermal distribution image predicting method
CN105841822A (en) * 2016-03-28 2016-08-10 安徽大学 Temperature on-line monitoring method for Stelmor-controlled cooling bulk coil rolling member
CN106199175A (en) * 2016-09-18 2016-12-07 武汉大学 Energy consumption testing based on temperature field and the method for analysis
CN106290394A (en) * 2016-09-30 2017-01-04 华南理工大学 A kind of cpu heat aluminium extruded forming defect detecting system and detection method
CN109655483A (en) * 2018-12-14 2019-04-19 四川大学 A kind of material microstructure defect inspection method based on deep learning algorithm
CN111862064A (en) * 2020-07-28 2020-10-30 桂林电子科技大学 Silver wire surface flaw identification method based on deep learning
CN113325001A (en) * 2021-04-12 2021-08-31 浙江花园新能源有限公司 Automatic distinguishing and detecting equipment and method for surface appearance flaws of copper foil
CN114226262A (en) * 2020-09-09 2022-03-25 宜谷京科技实业有限公司 Flaw detection method, flaw classification method and flaw detection system
CN114742770A (en) * 2022-03-22 2022-07-12 华中科技大学 Electrolytic copper plate copper nodule defect detection method, training method and system
CN115060376A (en) * 2022-06-09 2022-09-16 燕山大学 Aluminum alloy temperature field measuring method based on infrared thermal imaging and iterative algorithm
CN115953566A (en) * 2023-03-15 2023-04-11 深圳市普雷德科技有限公司 Characteristic analysis method, system and medium for infrared thermal imager

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103226731A (en) * 2013-03-20 2013-07-31 太原理工大学 Online thermal distribution image predicting method
CN105841822A (en) * 2016-03-28 2016-08-10 安徽大学 Temperature on-line monitoring method for Stelmor-controlled cooling bulk coil rolling member
CN106199175A (en) * 2016-09-18 2016-12-07 武汉大学 Energy consumption testing based on temperature field and the method for analysis
CN106290394A (en) * 2016-09-30 2017-01-04 华南理工大学 A kind of cpu heat aluminium extruded forming defect detecting system and detection method
CN109655483A (en) * 2018-12-14 2019-04-19 四川大学 A kind of material microstructure defect inspection method based on deep learning algorithm
CN111862064A (en) * 2020-07-28 2020-10-30 桂林电子科技大学 Silver wire surface flaw identification method based on deep learning
CN114226262A (en) * 2020-09-09 2022-03-25 宜谷京科技实业有限公司 Flaw detection method, flaw classification method and flaw detection system
CN113325001A (en) * 2021-04-12 2021-08-31 浙江花园新能源有限公司 Automatic distinguishing and detecting equipment and method for surface appearance flaws of copper foil
CN114742770A (en) * 2022-03-22 2022-07-12 华中科技大学 Electrolytic copper plate copper nodule defect detection method, training method and system
CN115060376A (en) * 2022-06-09 2022-09-16 燕山大学 Aluminum alloy temperature field measuring method based on infrared thermal imaging and iterative algorithm
CN115953566A (en) * 2023-03-15 2023-04-11 深圳市普雷德科技有限公司 Characteristic analysis method, system and medium for infrared thermal imager

Also Published As

Publication number Publication date
CN116500086A (en) 2023-07-28

Similar Documents

Publication Publication Date Title
CN116500086B (en) Deep learning-based copper complex aluminum heat dissipation bottom plate production evaluation method and system
CN109598721B (en) Defect detection method and device for battery pole piece, detection equipment and storage medium
JP7138753B2 (en) Image defect detection method, device, electronic device, storage medium and product
US6701615B2 (en) Inspection and sorting system and method for part repair
CN111815572B (en) Method for detecting welding quality of lithium battery based on convolutional neural network
CN110544231B (en) Lithium battery electrode surface defect detection method based on background standardization and centralized compensation algorithm
CN116188475B (en) Intelligent control method, system and medium for automatic optical detection of appearance defects
CN113592828B (en) Nondestructive testing method and system based on industrial endoscope
CN112529884B (en) Welding spot quality evaluation method based on indentation characteristic image recognition
CN114372955A (en) Casting defect X-ray diagram automatic identification method based on improved neural network
CN109900363A (en) A kind of object infrared measurement of temperature method and apparatus based on contours extract
TW201512649A (en) Method of chip detects inspecting, system therefor, and computer program product thereof
CN114881987A (en) Improved YOLOv 5-based hot-pressing light guide plate defect visual detection method
CN117152129B (en) Visual detection method and system for surface defects of battery cover plate
CN110880170A (en) Depth prediction method for composite material defects
CN111238927A (en) Fatigue durability evaluation method and device, electronic equipment and computer readable medium
CN111189906B (en) On-line intelligent judging and classifying identification method for alternating current magnetic field defects
Wang et al. Broad learning system based visual fault diagnosis for electrical equipment thermography images
CN114596296A (en) High-sensitivity hot-rolled steel coil end surface defect identification system and method
CN112767365A (en) Flaw detection method
Li et al. A quantitative analysis method for pipeline defect inspection based on infrared thermal imaging
Shin et al. Visualization for explanation of deep learning-based defect detection model using class activation map
Candra et al. Computer Vision Implementation in Scratch Inspection and Color Detection on The Car Roof Surface
CN116787022B (en) Heat dissipation copper bottom plate welding quality detection method and system based on multi-source data
CN114841921B (en) Thermal imaging uneven temperature background acquisition method and defect detection method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant